Package: isotree
Type: Package
Title: Isolation-Based Outlier Detection
Version: 0.1.18
Date: 2020-07-29
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/isotree
BugReports: https://github.com/david-cortes/isotree/issues
Description: Fast and multi-threaded implementation of
	isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>),
	extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>),
	SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>),
	and fair-cut forest (Cortes (2019) <arXiv:1911.06646>),
	for isolation-based outlier detection, clustered outlier detection, distance or similarity
	approximation (Cortes (2019) <arXiv:1910.12362>),
	and imputation of missing values (Cortes (2019) <arXiv:1911.06646>),
	based on random or guided decision tree splitting. Provides simple heuristics for fitting the model to
	categorical columns and handling missing data, and offers options for varying between random and guided
	splits, and for using different splitting criteria.
License: BSD_2_clause + file LICENSE
Imports: Rcpp (>= 1.0.1)
Suggests: MASS, outliertree, jsonlite, readr
Enhances: Matrix, SparseM
LinkingTo: Rcpp, Rcereal
RoxygenNote: 7.1.0
NeedsCompilation: yes
Packaged: 2020-07-29 18:03:44 UTC; david
Repository: CRAN
Date/Publication: 2020-07-29 18:30:02 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 05:07:05 UTC; windows
Archs: i386, x64
